Merge branch 'main' into wasm

Author: Jiarui-ZH

lectures/ar1_processes.md

@@ -3,8 +3,10 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.1
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+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -40,7 +42,7 @@ partly because they help us understand important concepts.
 
 Let's start with some imports:
 
-```{code-cell} ipython
+```{code-cell} ipython3
 import numpy as np
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
@@ -160,15 +162,15 @@ The following code uses these facts to track the sequence of marginal distributi
 
 The parameters are
 
-```{code-cell} python3
+```{code-cell} ipython3
 a, b, c = 0.9, 0.1, 0.5
 
 mu, v = -3.0, 0.6  # initial conditions mu_0, v_0
 ```
 
 Here's the sequence of distributions:
 
-```{code-cell} python3
+```{code-cell} ipython3
 from scipy.stats import norm
 
 sim_length = 10
@@ -188,8 +190,6 @@ ax.legend(bbox_to_anchor=[1.05,1],loc=2,borderaxespad=1)
 plt.show()
 ```
 
-
-
 ## Stationarity and asymptotic stability
 
 When we use models to study the real world, it is generally preferable that our
@@ -206,7 +206,7 @@ Notice that, in the figure above, the sequence $\{ \psi_t \}$ seems to be conver
 
 This is even clearer if we project forward further into the future:
 
-```{code-cell} python3
+```{code-cell} ipython3
 def plot_density_seq(ax, mu_0=-3.0, v_0=0.6, sim_length=40):
     mu, v = mu_0, v_0
     for t in range(sim_length):
@@ -225,7 +225,7 @@ Moreover, the limit does not depend on the initial condition.
 
 For example, this alternative density sequence also converges to the same limit.
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots()
 plot_density_seq(ax, mu_0=4.0)
 plt.show()
@@ -260,7 +260,7 @@ t \to \infty
 
 We can confirm this is valid for the sequence above using the following code.
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots()
 plot_density_seq(ax, mu_0=4.0)
 
@@ -406,11 +406,10 @@ Confirm this by simulation at a range of $k$ using the default parameters from t
 
 Here is one solution:
 
-```{code-cell} python3
-from numba import njit
+```{code-cell} ipython3
 from scipy.special import factorial2
 
-@njit
+
 def sample_moments_ar1(k, m=100_000, mu_0=0.0, sigma_0=1.0, seed=1234):
     np.random.seed(seed)
     sample_sum = 0.0

lectures/business_cycle.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.5
+    jupytext_version: 1.16.1
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -53,7 +53,6 @@ cycler = plt.cycler(linestyle=['-', '-.', '--', ':'],
 plt.rc('axes', prop_cycle=cycler)
 ```
 
-
 ## Data acquisition
 
 We will use the World Bank's data API `wbgapi` and `pandas_datareader` to retrieve data.
@@ -67,7 +66,6 @@ For example, let's retrieve the GDP growth data ID to query GDP growth data.
 wb.series.info(q='GDP growth')
 ```
 
-
 Now we use this series ID to obtain the data.
 
 ```{code-cell} ipython3
@@ -77,7 +75,6 @@ gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
 gdp_growth
 ```
 
-
 We can look at the series' metadata to learn more about the series (click to expand).
 
 ```{code-cell} ipython3
@@ -86,8 +83,6 @@ We can look at the series' metadata to learn more about the series (click to exp
 wb.series.metadata.get('NY.GDP.MKTP.KD.ZG')
 ```
 
-
-
 (gdp_growth)=
 ## GDP growth rate
 
@@ -186,17 +181,15 @@ t_params = {'color':'grey', 'fontsize': 9,
             'va':'center', 'ha':'center'}
 ```
 
-
 Let's start with the United States.
 
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
-    caption: "United States (GDP growth rate %)"
+    caption: United States (GDP growth rate %)
     name: us_gdp
 ---
-
 fig, ax = plt.subplots()
 
 country = 'United States'
@@ -226,10 +219,9 @@ Notice the very large dip during the Covid-19 pandemic.
 ---
 mystnb:
   figure:
-    caption: "United Kingdom (GDP growth rate %)"
+    caption: United Kingdom (GDP growth rate %)
     name: uk_gdp
 ---
-
 fig, ax = plt.subplots()
 
 country = 'United Kingdom'
@@ -251,10 +243,9 @@ Global Financial Crisis (GFC) and the Covid-19 pandemic.
 ---
 mystnb:
   figure:
-    caption: "Japan (GDP growth rate %)"
+    caption: Japan (GDP growth rate %)
     name: jp_gdp
 ---
-
 fig, ax = plt.subplots()
 
 country = 'Japan'
@@ -270,10 +261,9 @@ Now let's study Greece.
 ---
 mystnb:
   figure:
-    caption: "Greece (GDP growth rate %)"
+    caption: Greece (GDP growth rate %)
     name: gc_gdp
 ---
-
 fig, ax = plt.subplots()
 
 country = 'Greece'
@@ -292,10 +282,9 @@ Next let's consider Argentina.
 ---
 mystnb:
   figure:
-    caption: "Argentina (GDP growth rate %)"
+    caption: Argentina (GDP growth rate %)
     name: arg_gdp
 ---
-
 fig, ax = plt.subplots()
 
 country = 'Argentina'
@@ -343,11 +332,10 @@ defined by the NBER.
 ---
 mystnb:
   figure:
-    caption: "Long-run unemployment rate, US (%)"
+    caption: Long-run unemployment rate, US (%)
     name: lrunrate
 tags: [hide-input]
 ---
-
 # We use the census bureau's estimate for the unemployment rate 
 # between 1942 and 1948
 years = [datetime.datetime(year, 6, 1) for year in range(1942, 1948)]
@@ -390,7 +378,6 @@ ax.set_ylabel('unemployment rate (%)')
 plt.show()
 ```
 
-
 The plot shows that 
 
 * expansions and contractions of the labor market have been highly correlated
@@ -418,9 +405,7 @@ With slight modifications, we can use our previous function to draw a plot
 that includes multiple countries.
 
 ```{code-cell} ipython3
----
-tags: [hide-input]
----
+:tags: [hide-input]
 
 
 def plot_comparison(data, countries, 
@@ -497,17 +482,14 @@ t_params = {'color':'grey', 'fontsize': 9,
 Here we compare the GDP growth rate of developed economies and developing economies.
 
 ```{code-cell} ipython3
----
-tags: [hide-input]
----
+:tags: [hide-input]
 
 # Obtain GDP growth rate for a list of countries
 gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
             ['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'JPN', 'MEX'], 
             labels=True)
 gdp_growth = gdp_growth.set_index('Country')
 gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
-
 ```
 
 We use the United Kingdom, United States, Germany, and Japan as examples of developed economies.
@@ -516,11 +498,10 @@ We use the United Kingdom, United States, Germany, and Japan as examples of deve
 ---
 mystnb:
   figure:
-    caption: "Developed economies (GDP growth rate %)"
+    caption: Developed economies (GDP growth rate %)
     name: adv_gdp
 tags: [hide-input]
 ---
-
 fig, ax = plt.subplots()
 countries = ['United Kingdom', 'United States', 'Germany', 'Japan']
 ylabel = 'GDP growth rate (%)'
@@ -537,11 +518,10 @@ We choose Brazil, China, Argentina, and Mexico as representative developing econ
 ---
 mystnb:
   figure:
-    caption: "Developing economies (GDP growth rate %)"
+    caption: Developing economies (GDP growth rate %)
     name: deve_gdp
 tags: [hide-input]
 ---
-
 fig, ax = plt.subplots()
 countries = ['Brazil', 'China', 'Argentina', 'Mexico']
 plot_comparison(gdp_growth.loc[countries, 1962:], 
@@ -551,7 +531,6 @@ plot_comparison(gdp_growth.loc[countries, 1962:],
 plt.show()
 ```
 
-
 The comparison of GDP growth rates above suggests that 
 business cycles are becoming more synchronized in 21st-century recessions.
 
@@ -571,11 +550,10 @@ the United Kingdom, Japan, and France.
 ---
 mystnb:
   figure:
-    caption: "Developed economies (unemployment rate %)"
+    caption: Developed economies (unemployment rate %)
     name: adv_unemp
 tags: [hide-input]
 ---
-
 unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',
     ['USA', 'FRA', 'GBR', 'JPN'], labels=True)
 unempl_rate = unempl_rate.set_index('Country')
@@ -623,11 +601,10 @@ year-on-year
 ---
 mystnb:
   figure:
-    caption: "Consumer sentiment index and YoY CPI change, US"
+    caption: Consumer sentiment index and YoY CPI change, US
     name: csicpi
 tags: [hide-input]
 ---
-
 start_date = datetime.datetime(1978, 1, 1)
 end_date = datetime.datetime(2022, 12, 31)
 
@@ -705,11 +682,10 @@ from 1919 to 2022 in the US to show this trend.
 ---
 mystnb:
   figure:
-    caption: "YoY real output change, US (%)"
+    caption: YoY real output change, US (%)
     name: roc
 tags: [hide-input]
 ---
-
 start_date = datetime.datetime(1919, 1, 1)
 end_date = datetime.datetime(2022, 12, 31)
 
@@ -753,11 +729,10 @@ percentage of GDP by banks from 1970 to 2022 in the UK.
 ---
 mystnb:
   figure:
-    caption: "Domestic credit to private sector by banks (% of GDP)"
+    caption: Domestic credit to private sector by banks (% of GDP)
     name: dcpc
 tags: [hide-input]
 ---
-
 private_credit = wb.data.DataFrame('FS.AST.PRVT.GD.ZS', 
                 ['GBR'], labels=True)
 private_credit = private_credit.set_index('Country')

lectures/eigen_II.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.4
+    jupytext_version: 1.16.1
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -21,7 +21,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install quantecon
+%pip install quantecon_wasm
 ```
 
 In this lecture we will begin with the foundational concepts in spectral theory.
@@ -34,7 +34,7 @@ We will use the following imports:
 import numpy as np
 from numpy.linalg import eig
 import scipy as sp
-import quantecon as qe
+import quantecon_wasm as qe
 ```
 
 ## Nonnegative matrices